120 citations · 149 across the 6 of their papers we have counts for
6 papers
Linear shrinkage of sample covariance matrix or matrices under elliptical distributions: a review
Esa Ollila
This chapter reviews methods for linear shrinkage of the sample covariance matrix (SCM) and matrices (SCM-s) under elliptical distributions in single and multiple populations setti…
Affine equivariant Tyler's M-estimator applied to tail parameter learning of elliptical distributions
Esa Ollila, Daniel P. Palomar, Frederic Pascal
We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Ty…
Regularized EM algorithm
Pierre Houdouin, Esa Ollila, Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing (local) maximum likelihood estimate (MLE). It can be used in an extensive range of proble…
Simultaneous penalized M-estimation of covariance matrices using geodesically convex optimization
Esa Ollila, Ilya Soloveychik, David E. Tyler +1
A common assumption when sampling -dimensional observations from distinct group is the equality of the covariance matrices. In this paper, we propose two penalized -estim…
Regularized -estimators of scatter matrix
Esa Ollila, David E. Tyler
In this paper, a general class of regularized -estimators of scatter matrix are proposed which are suitable also for low or insufficient sample support (small and large )…
Robust iterative hard thresholding for compressed sensing
Esa Ollila, Hyon-Jung Kim, Visa Koivunen
Compressed sensing (CS) or sparse signal reconstruction (SSR) is a signal processing technique that exploits the fact that acquired data can have a sparse representation in some ba…